code: def testPrecomputedDivisorCompositeTensor(self):
      base_kernel = exponentiated_quadratic.ExponentiatedQuadratic(
          [1.0, 2.0]
      )
      fixed_inputs = tf.ones([0, 2], np.float32)
      schur_with_divisor = (
          schur_complement.SchurComplement.with_precomputed_divisor(
              base_kernel, fixed_inputs
          )
      )
  
      x = np.ones([4, 3], np.float32)
      y = np.ones([5, 3], np.float32)
  
      flat = tf.nest.flatten(schur_with_divisor, expand_composites=True)
      unflat = tf.nest.pack_sequence_as(
          schur_with_divisor, flat, expand_composites=True
      )
      self.assertIsInstance(unflat, schur_complement.SchurComplement)
      self.assertIsNotNone(
          schur_with_divisor._precomputed_divisor_matrix_cholesky
      )
  
      actual = self.evaluate(schur_with_divisor.matrix(x, y))
  
      self.assertAllClose(self.evaluate(unflat.matrix(x, y)), actual)
  
      @tf.function
      def matrix(k):
          return k.matrix(x, y)
  
      self.assertAllClose(actual, matrix(schur_with_divisor))
      self.assertAllClose(actual, matrix(unflat))
  
imports: import functools
  import itertools
  from unittest import mock
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import cholesky_util
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.math.psd_kernels import exponentiated_quadratic
  from tensorflow_probability.python.math.psd_kernels import schur_complement
  from tensorflow_probability.python.math.psd_kernels.internal import test_util
label_desc: Use image node_id 11 for calling the SchurComplementTest obj's underlying member method code with example usage: obj.testPrecomputedDivisorCompositeTensor() and returns: k